Segmentation of Partially Overlapping Convex Objects Using Branch and Bound Algorithm

This paper presents a novel method for the segmentation of partially overlapping convex shape objects in silhouette images. The proposed method involves two main steps: contour evidence extraction and contour estimation. Contour evidence extraction starts by recovering contour segments from a binarized image using concave contour point detection. The contour segments which belong to the same objects are grouped by utilizing a criterion defining the convexity, symmetry and ellipticity of the resulting object. The grouping is formulated as a combinatorial optimization problem and solved using the well-known branch and bound algorithm. Finally, the contour estimation is implemented through a non-linear ellipse fitting problem in which partially observed objects are modeled in the form of ellipse-shape objects. The experiments on a dataset of consisting of nanoparticles demonstrate that the proposed method outperforms four current state-of-art approaches in overlapping convex objects segmentation. The method relies only on edge information and can be applied to any segmentation problems where the objects are partially overlapping and have an approximately convex shape.